SYLLABUS
Contact Information:

Time	MWF: 8 - 9 AM
Place	213 Simrall
Instructor	Joseph Picone, Professor
Office	413 Simrall
Office Hours	9-10 MWF (others by appt.)
Email	picone@cavs.msstate.edu
Class Alias	ece_8443@cavs.msstate.edu
URL	http://www.cavs.msstate.edu/research/isip/publications/courses/ece_8443
Textbook	R.O. Duda, P.E. Hart, and D.G. Stork, Pattern Classification, Second Edition, Wiley Interscience, ISBN: 0-471-05669-3, 2000 (supporting material available at http://rii.ricoh.com/~stork/DHS.html)
Suggested Reference	D. MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge University Press, 2003 (also available at http://www.inference.phy.cam.ac.uk/mackay/itprnn/book.html)
Prerequisite	Statistics, Signal Processing, many years of research, and attendance at a lecture where the speaker proudly exclaimed "PCA Fails!"
Other Reference Materials	Pattern Recognition in Java: an applet designed to demonstrate fundamental concepts of pattern recognition. Internet Links: Godfried T. Toussaint's online resources that include lots of valuable links and course notes. Human Computer Interface Design: a new multi-university course sponsored by the National Science Foundation.

Grading Policies:

Item:	Date:	Weight:
Exam No. 1	02/16	25%
Exam No. 2	03/12	25%
Exam No. 3	04/23	25%
Final Exam (Paper)	05/04 (3 PM to 6 PM)	25%

Exams:

We will have three in-class exams in this course. Each will be closed books and notes. You will be allowed one page (double-sided) of notes. Calculators or other computing devices will not be allowed (or needed). The exams will require critical thinking. They will consist of one to two problems for which you will supply extended written solutions. The quality and clarity of your solution will be as important as whether you obtained the correct answer.

Final Exam:

For the final exam, you will select an algorithm, perform a blind evaluation of the algorithm using the data provided by the instructor, and write a four-page paper analyzing the results. Grades will be assigned based on how well you do relative to published performance and your peers, as well as the quality of your analysis. This will be explained in greater detail in class.

Attendance / Survival:

Attendance does not figure directly into your grade. However, historically students who do not attend class regularly do not do well in this class. Further, most students end up with a grade on a borderline at the end of the semester. In this case, I use attendance and classroom participation to determine the final grade.

Schedule:

Class	Date	Sections	Topic
1	01/12	1.1	Course Overview
2	01/14	1.2, 1.3	Introduction
3	01/16	1.4 - 1.6	Typical Applications
4	01/21	2.1	Bayes' Decision Theory
5	01/23	2.2, 2.3	Continuous Features; Minimum Classification Error
6	01/26	2.4	Decision Surfaces; Normal Distributions
7	01/28	2.5	Support Regions; Whitening Transformations
8	01/30	2.6	Discriminant Functions for the Normal Density
9	02/02	2.8, 2.9	Error Bounds; Discrete Features
10	02/04	3.1-3.2	Maximum Likelihood Parameter Estimation
11	02/06	3.4	Bayesian Parameter Estimation - Univariate
12	02/09	3.4	Bayesian Parameter Estimation - Multivariate
13	02/11	3.5	Bayesian Parameter Estimation - General Theory
14	02/13	3.6, 3.7	Problems of Dimensionality
15	02/16	Exam No. 1	Sections 1.1 - 2.9
16	02/18	3.8	Multiple Discriminant Analysis
17	02/20	3.9	The Expectation Maximization Algorithm (EM)
18	02/23	3.10	What is a "hidden" Markov model?
19	02/25	3.10	Fundamentals of HMMs
20	02/27	3.10	HMM Paramter Estimation
21	03/01	N/A	Review of Chapter 2
22	03/03	N/A	Review of Chapter 2
23	03/05	N/A	Review of Chapter 2
24	03/08	N/A	Review of Chapter 3
25	03/10	N/A	Review of Chapter 3
26	03/12	Exam No. 2	Sections 3.1 - 3.10
27	03/22	4.1 - 4.3	Parzen Windows
28	03/24	4.4 - 4.9	k-Nearest Neighbor
29	03/26	5.1 - 5.3	Linear Discriminant Functions
30	03/29	5.11	Support Vector Machines

23	03/05	5.7 - 5.12	Support Vector Machines
24	03/08	6.1 - 6.3	Multilayer Networks, Backpropagation
25	03/10	6.4 - 6.9	Bayes Theory, Practical Issues
27	03/22	7.1 - 7.2	Simulated Annealing
28	03/24	7.3 - 7.4	Boltzmann Machines
29	03/26	7.5 - 7.6	Evolutionary Methods
30	03/29	8.1 - 8.2	Decision Trees
31	03/31	8.3 - 8.4	CART
32	04/02	8.5	String Matching
33	04/05	8.6	Grammatical Methods
34	04/07	8.7	Rule Based Systems
35	04/12	9.1	Occam's Razor
36	04/14	9.2	No Free Lunch Theorem
37	04/16	9.3	Minimum Description Length
38	04/19	9.4 - 9.5	Resampling Techniques
39	04/21	9.6 - 9.7	Comparing and Combining Classifiers
40	04/23	Exam No. 3	TBD
41	04/26	10.4 - 10.7	Clustering
42	04/28	10.8 - 10.14	Advanced Clustering Techniques
43	04/30	Review	Comprehensive Review
44	05/04	Final Exam	3 PM to 6 PM

Homework:

Homework will be assigned but not graded. Solutions will be available online.

Number	Due Date	Chapter/Problem	Student(s)
1	01/23	Chapter 1: pattern recognition examples	Shrestha, Raghavan, Menon, Anand, Chandra
2	01/30	Chapter 2: 2, 3, 8, 10	Gao, Stanley
3	02/02	Chapter 2: 15, 21, 23, 24	Menon, Shrestha
4	02/09	Chapter 2: 37, 40, 43, 44	Anand, Raghavan
5	02/16	Chapter 3: 1, 4, 11, 13, 16	Chandra, Gao
6	02/23	Chapter 3: 17, 24, 38, 41, 50	Stanley, Menon
7	03/29	Chapter 4: 2, 3, 10, 13, 14	Shrestha, Anand
8	TBD	TBD	TBD
9	TBD	TBD	TBD
10	TBD	TBD	TBD